---
title: "Key U.S. Economic Indicators"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

 





```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(fredr)
library(fontawesome)
library(dygraphs)
library(extrafont)

fredr_set_key("9100cdf3c862283007d8c83ce02059e6")

recessions <-fredr(
    series_id = "JHDUSRGDPBR"
)

recessions_start_end <- recessions %>% # from https://datavizm20.classes.andrewheiss.com/example/11-example/
    mutate(recession_change = value - lag(value)) %>% 
    filter(recession_change != 0)

recs <- tibble(start = filter(recessions_start_end, recession_change == 1)$date,
               end = filter(recessions_start_end, recession_change == -1)$date)

```

```{r import values}

# Federal Funds Rate

ffr <- fredr(
  series_id = "FEDFUNDS"
)

#  US Regular All Formulations Gas Price
gas <- fredr(
  series_id = "GASREGW"
)

#  University of Michigan: Consumer Sentiment
## 1966Q1 = 100 Index

conssent <- fredr(
  series_id = "UMCSENT"
)

#  Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma; Dollars per Barrel
oil <- fredr(
  series_id = "DCOILWTICO"
)

# Percent, Not Seasonally Adjusted;  Weekly, Ending Thursday 
mortgage <- fredr(
  series_id = "MORTGAGE30US"
)

# Consumer Price Index for All Urban Consumers: All Items in U.S. City Average 
# Index 1982-1984=100, Seasonally Adjusted 
# pc1 = percent change from 1 year ago, see https://cran.r-project.org/web/packages/fredr/fredr.pdf
cpi <- fredr(
  series_id = "CPIAUCSL", units = "pc1"
)
```

```{r get-latest-value-and-date-functions}
get_latest_value = function(input){
  input %>%
    arrange(desc(date)) %>%
    slice(1) %>%
    pull(value)
}

get_latest_date = function(input){
  input %>%
    arrange(desc(date)) %>%
    slice(1) %>%
    pull(date) -> temp
    paste(lubridate::month(temp, label = TRUE),
            lubridate::day(temp),
            lubridate::year(temp))
}
```

```{r}
# Fed Funds Rate
ffr_latest <- paste0(get_latest_value(ffr),"%")
ffr_latest_date <- get_latest_date(ffr)

# Gas Price
gas_latest <- paste0("$", round(get_latest_value(gas), 2), "/gallon")
gas_latest_date <- get_latest_date(gas)

# Oil Price
oil_latest <- paste0("$", round(get_latest_value(oil), 2), "/barrel")
oil_latest_date <- get_latest_date(oil)

# Fed Funds Rate
cpi_latest <- paste0(get_latest_value(cpi),"%")
cpi_latest_date <- get_latest_date(cpi)

```

```{r, eval=F}
ffr_current <- ffr %>%
  arrange(desc(date)) %>%
  slice(1) %>%
  pull(value) %>%
  paste0(., "%")

ffr_current_date <- ffr %>%
  arrange(desc(date)) %>%
  slice(1) %>%
  pull(date)
```

Indicators at a Glance
===============

Row
---------------

### Federal Funds Rate (as of `r ffr_latest_date`) {.value-box}

```{r}
valueBox(value = ffr_latest, icon = "fa-percent")
```

### Gas Price (as of `r gas_latest_date`) {.value-box}

```{r}
valueBox(value = gas_latest, icon = "fa-gas-pump")
```

### Oil Price (as of `r oil_latest_date`) {.value-box}

```{r}
valueBox(value = oil_latest, icon = "fa-oil-well")
```

### Consumer Price Index — Percent Change from 1 Year Ago (as of `r cpi_latest_date`) {.value-box}

```{r}
valueBox(value = cpi_latest, icon = "fa-solid fa-money-bill-trend-up")
```

Key Prices
=======================================================================

Column {data-width=650}
-----------------------------------------------------------------------

### Federal Funds Rate

```{r}
plot_ffr <-ggplot(data = ffr)+
    aes(x = date,
        y = value,
        color = series_id)+
    geom_rect(data = recs, 
              aes(xmin = start, xmax = end, ymin = -Inf, ymax = Inf),
              inherit.aes = FALSE, fill = "black", alpha = 0.3)+
    geom_path(size=1)+
    geom_hline(yintercept=0, size=1)+
    scale_x_date(limits=as.Date(c("1955-01-01", "2022-07-01")),
                 date_breaks = "5 years",
                 #minor_breaks = "2 years",
                 date_labels = "%Y",
                 expand = c(0,0))+
    scale_y_continuous(breaks=seq(0,20,2),
                       labels=function(x){paste0(x,"%")},
                       limits = c(0,20),
                       expand = c(0,0))+
    labs(x = "Year",
         y = "Federal Funds Rate",
         caption = "Data Source: FRED; Recessions Shaded in Gray")+
    theme_classic(base_family = "Fira Sans Condensed", base_size = 16)+
    theme(legend.position="none")

plot_ffr # %>% plotly::ggplotly()
```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}
library(xts)
ffr %>% 
  select(value) %>%
  rename("Fed Funds Rate" = value) %>% 
  xts(., order.by = as.Date(ffr$date)) %>% 
  dygraph() %>%
  dyRangeSelector()
```

### Chart C

```{r}
library(highcharter)
hchart(ffr, "line", hcaes(x = date, y = value))
  
```

Gasoline
===============

Column {data-width=650}
-----------------------------------------------------------------------

### Gasoline

```{r}
plot_gas <-ggplot(data = gas)+
    aes(x = date,
        y = value,
        color = series_id)+
    geom_rect(data = recs, 
              aes(xmin = start, xmax = end, ymin = -Inf, ymax = Inf),
              inherit.aes = FALSE, fill = "black", alpha = 0.3)+
    geom_path(size=1)+
    geom_hline(yintercept=0, size=1)+
    scale_x_date(limits=as.Date(c("1990-01-01", gas_latest_date)),
                 date_breaks = "2 years",
                 minor_breaks = "2 years",
                 date_labels = "%Y",
                 expand = c(0,0))+
    scale_y_continuous(breaks=seq(0,5.25,0.25),
                       labels=scales::dollar_format(),
                       limits = c(0,5.25),
                       expand = c(0,0))+
    labs(x = "Year",
         y = "Price/gal",
         subtitle = "US Regular All Formulations Gas Price",
         caption = "Data Source: FRED; Recessions Shaded in Gray")+
    ggthemes::theme_pander(base_family = "Fira Sans Condensed", base_size = 16)+
    theme(legend.position="none")

plot_gas #%>% plotly::ggplotly()
```

Column {data-width=650}
-----------------

### Oil

```{r}
plot_oil <- ggplot(data = oil)+
    aes(x = date,
        y = value,
        color = series_id)+
    geom_rect(data = recs, 
              aes(xmin = start, xmax = end, ymin = -Inf, ymax = Inf),
              inherit.aes = FALSE, fill = "black", alpha = 0.3)+
    geom_path(size=1)+
    geom_hline(yintercept=0, size=1)+
    scale_x_date(limits=as.Date(c("1950-01-01", oil_latest_date)),
                 date_breaks = "5 years",
                 minor_breaks = "2 years",
                 date_labels = "%Y",
                 expand = c(0,0))+
    scale_y_continuous(breaks=seq(0,150,20),
                       labels=scales::dollar_format(),
                       limits = c(0,150),
                       expand = c(0,0))+
    labs(x = "Year",
         y = "Price/barrel",
         subtitle = "Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma",
         caption = "Data Source: FRED; Recessions Shaded in Gray")+
    ggthemes::theme_pander(base_family = "Fira Sans Condensed", base_size = 16)+
    theme(legend.position="none")

plot_oil #%>% plotly::ggplotly()

```